An indirect RUL prognosis for lithium-ion battery under vibration stress using Elman neural network

Li, Wenhua; Jiao, Zhipeng; Du, Ling; Wang, Fan; Zhu, Yazun

doi:10.1016/j.ijhydene.2019.03.101

Cited by 83 publications

(30 citation statements)

References 28 publications

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“…where R th represents the threshold of the RUL, which will be explained in Section 3.1. Equations (2) and (3) indicate that RUL i j is dependent on the equipment's previous state and sensor data variation. Our goal is to learn a non-linearity function (Equation (4)) and minimize the error (Equation (5)) between the prediction value and the true RUL.…”

Section: Problem Definitionmentioning

confidence: 99%

“…The complexity of the equipment involved in modern industry has rapidly increased in the past decades [1]. Any failure of equipment may cause catastrophic consequences [2,3], and reliability and maintenance are key for equipment [4]. Therefore, it's essential to have an effective strategy that positively coordinates scheduling and maintenance to ensure productivity, personal safety and manufacturing development [5].…”

Section: Introductionmentioning

confidence: 99%

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Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

et al. 2019

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Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal dependency features. Furthermore, a fully connected neural network is employed to achieve a regression model of RUL prognostics. Lastly, validated with the aid of numerical and graphic results by an equipment RUL dataset from the Commercial Modular Aero-Propulsion System Simulation(C-MAPSS), the proposed scheme turns out to be superior to four existing models regarding accuracy and effectiveness.

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Section: Problem Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

et al. 2019

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“…Carrol et al [16] used ANN, support vector machine (SVM), and logistic regression to predict the remaining life of a gearbox and found that the ANN method has the highest prediction accuracy. Li et al [17] established a RUL prediction model for lithium batteries based on Elman neural network and verified the feasibility of the model in prediction. Ordonez et al [18] proposed a prediction model based on kernel principal component analysis (KPCA) and gated recursive unit (GRU) suitable for the life prediction of complex systems and verified the accuracy of the method through aeronautical propulsion system simulation data.…”

Section: Introductionmentioning

confidence: 98%

Remaining Useful Life Prediction of Rolling Bearings Using Electrostatic Monitoring Based on Two-Stage Information Fusion Stochastic Filtering

Zhang

Wang

2020

Mathematical Problems in Engineering

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The accurate prediction of the remaining useful life (RUL) of rolling bearings is of great significance for a rational formulation of maintenance strategies and the reduction of maintenance costs. According to the two-stage nonlinear degradation characteristics of rolling bearing operation, this paper proposes a prognosis model based on modified stochastic filtering. First, multiple features reextracted from the time domain, frequency domain, and complexity angles, and the baseline Gaussian mixture model (GMM) is established using the normal operating data after spectral regression. The Bayesian-inferred distance (BID) is used as a quantitative indicator to reflect the bearing performance degradation degree. Then, taking multiparameter fusion results as input, the relationship between BID and remaining life is established by the two-stage stochastic filtering model to realize online dynamic remaining useful life prediction. The method in this paper overcomes the difficulty of accurately defining the failure threshold of rolling bearing. At the same time, it reduces the computational burden, avoiding the need of calculating the joint probability distribution for high-dimensional data. Finally, the proposed method has been verified experimentally to have high precision and engineering application value.

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“…At present, there are many methods for predicting the remaining life of lithium-ion battery based on the datadriven method. For example, Li et al [18] proposed a method for predicting the remaining life of indirect lithium-ion batteries based on Elman neural network. Cadini et al [11] proposed a particle filter based residual life prediction diagnosis method for lithium-ion batteries.…”

Section: Introductionmentioning

confidence: 99%

“…Small sample refers to the situation in which the number of samples is small, which is the standard for judging the quality of results in existing studies. In practical applications, the threshold value of small sample problem is usually defined as 30 [11], [17], [18]. Specifically, in fields of medical diagnosis or industrial manufacturing, the problem of small data volume exists due to the lack of prior experience data or the difficulty in obtaining available data [8].…”

Section: Introductionmentioning

confidence: 99%

A Virtual Sample Generation Method Based on Differential Evolution Algorithm for Overall Trend of Small Sample Data: Used for Lithium-ion Battery Capacity Degradation Data

Kang

Guan

et al. 2019

IEEE Access

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Considering the wide application of lithium-ion battery in life, the prediction of the remaining life of lithium-ion battery has become a research hotspot. Studies show, due to the improvement of the technology level of lithium-ion battery, its life is getting longer and longer. Even under the condition of accelerated life test, it is difficult to obtain enough available data for research in a short term. In order to solve the problem of how to accurately predict the residual life with the data-driven method under the condition of small sample size, an overall trend virtual sample generation method based on differential evolution (OT-DEVSG) is proposed. This method uses a differential evolution algorithm with better optimization performance, and improves the original mega-trend-diffusion (MTD) method, the range of virtual samples is effectively constrained and the trend of samples can be estimated more accurately. The method can effectively generate a virtual sample data sequence with time parameters, and adapt the virtual sample to the real-life sample trend, which solves the problem of insufficient degradation data of the lithium-ion batteries. Finally, we validate the effectiveness of the OT-DEVSG method with three existing data sets. The experimental results show that the proposed OT-DEVSG method is effective for solving the problem of long-term life prediction of lithium-ion batteries.INDEX TERMS Lithium-ion battery, overall trend virtual sample generation method based on differential evolution (OT-DEVSG), small data.

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An indirect RUL prognosis for lithium-ion battery under vibration stress using Elman neural network

Cited by 83 publications

References 28 publications

Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

Remaining Useful Life Prediction of Rolling Bearings Using Electrostatic Monitoring Based on Two-Stage Information Fusion Stochastic Filtering

A Virtual Sample Generation Method Based on Differential Evolution Algorithm for Overall Trend of Small Sample Data: Used for Lithium-ion Battery Capacity Degradation Data

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